ANALYSIS AND DATA MINING APPLICATION DESIGN FOR DRUG STOCK FORECASTING AT KLINIK XYZ
Keywords:
Forecasting, Artificial Neural Network, Drug StocksAbstract
Forecasting is an important tool for making management planning and decision making. In this study, forecasting will help Klinik XYZ in making stock inventory decisions so that there won’t be any shortage or excess inventory. By utilizing data mining, the authors aim is to implement artificial neural network to predict drug stock inventories and also design an application that is able to apply forecasting using artificial neural network methods to predict drug stocks. Using an artificial neural network model with configurations of 5 neurons in the input layer, 3 hidden layers with 4, 3, and 4 neurons respectively, and 1 neuron in the output layer with a ReLU as its activation function and learning rate of 0.001, this application is able to presents forecasting results for the desired period accompanied by forecast error values in the form of Mean Absolute Percentage Error. From the 4 types of products tested with a total sample of 24 periods from January 2017 to December 2018, artificial neural network models provide predictive results for September 2018 - December 2018 are: (1) for Amobiotic products are 593, 693, 584, and 632, (2) Loremid products are 97, 222, 161 and 137, (3) Meproson products are 599, 614, 398 and 401, (4) and Nikolam products are 215, 256, 290, and 338.
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